The news of IBM acquiring Confluent for $11 billion has caused quite a stir. This isn’t a revolution that changes the laws of physics in IT, but rather a very pragmatic completion of a portfolio by a giant. IBM has been servicing the critical infrastructure of the largest corporations for years, and this purchase is a signal that data streaming is finally ceasing to be a novelty and is becoming the standard for connecting old worlds with new ones. To understand the sense behind this transaction, we must first go down to the foundational level and explain what Apache Kafka actually is and why it has become so important.
What is Data Streaming and Kafka?
In the traditional data processing model, the batch approach dominated: information was collected in a database and crunched cyclically, for example, once a day at night. Data streaming, for which Apache Kafka has become synonymous, is a fundamental shift in this approach.
In simple terms, Kafka is a distributed, immutable commit log. It acts as a central nervous system that accepts messages about events the moment they occur and makes them available to interested systems in real-time.
A key feature of Kafka is its role as a buffer that decouples data producers from their consumers. Thanks to this, a rapid influx of information does not “kill” the target systems, which can process data at their own pace. It is this mechanism that allows companies to move from analyzing what happened yesterday to reacting to what is happening in this very millisecond. It is not just a faster database, but a completely different way of thinking about information flow in system architecture.
Are Mainframes, IBM Power, and DB2 Back in the Game?
Here we arrive at the most interesting aspect of IBM’s acquisition of Confluent: the integration of legacy systems. Many key financial and insurance institutions still base their operations on reliable but closed mainframe environments, IBM Power servers, and massive databases like DB2 or Oracle. These systems handle transactionality excellently but are difficult to integrate with modern web or mobile applications. Attempts to replace or rewrite them usually end in spectacular failures and enormous costs.
Kafka—or more precisely, the ecosystem of Confluent connectors—acts here as a digital bypass. Using the Change Data Capture (CDC) mechanism, Kafka can listen directly to the transaction logs of a DB2 or Oracle database without burdening the main database engine with SQL queries. Every change in the legacy system, such as a deposit into an account or a change in policy status, is immediately captured and emitted as an event to Kafka. Thanks to this, modern business logic, written in microservices in the cloud, can react to events from the mainframe in a fraction of a second, without the need to interfere with that old, monolithic code. By buying Confluent, IBM gives its clients a ready-made tool to “open up” their most valuable data without a risky infrastructure revolution.
Kora: The Technical Justification for the Price
From an engineer’s perspective, IBM didn’t just buy a brand, but primarily the technology that solves the biggest pain points of “pure” open-source Kafka: the Kora engine.
In standard Kafka, the compute layer and the storage layer are tightly coupled on the broker disks. This makes scaling the cluster or replacing nodes during a failure a costly and time-consuming process involving copying terabytes of data over the network.
Architecturally, Kora separates these two layers. Data is offloaded to cheap and infinitely scalable object storage (such as S3), and the brokers themselves become light and stateless. This is an engineering concrete. It allows for instant scaling of computing power up or down depending on traffic, as well as storing event history practically indefinitely at low costs. Kafka ceases to be just a transmission pipe and becomes a System of Record, which is crucial for audit and analytical purposes in large corporations.
A Nervous System for AI and Hybrid Cloud
Finally, it is worth mentioning the context of Artificial Intelligence and the hybrid cloud. To be useful in business, AI models must operate on current context, not knowledge from a week ago. Kafka provides this context in real-time, feeding RAG (Retrieval-Augmented Generation) mechanisms with fresh data.
At the same time, thanks to the “Bring Your Own Cloud” model offered by Confluent, companies can process this data within their own private networks (VPC), maintaining data sovereignty. This fits perfectly into IBM’s strategy, which is building a consistent technology stack independent of whether the client uses AWS, Azure, or their own server room based on IBM Power. This transaction is, therefore, a logical completion of the puzzle where modernity meets the stability of enterprise systems.
The Verdict for the Enterprise Market
Looking at this move from a broader perspective, we see that IBM has ultimately cemented the role of data streaming as the foundation of modern IT. This acquisition closes academic discussions on whether it is worth investing in event-driven architecture—now the question is only how quickly to implement it as a standard.
For engineers and architects, this means stability and access to an ecosystem that can connect the armored cabinets of mainframes with generative AI algorithms without the need for “jerry-rigging” risky workarounds. IBM, by combining Red Hat and Confluent in one portfolio, has effectively become the provider of a complete circulatory system for enterprises, confirming an old engineering truth: in the long run, the winner is the one who controls the flow of data, not just its resting place